Collapse Factors

Collapses factors of type factor, ordered: Collapses the rarest factors in the training samples, until target_level_count levels remain. Levels that have prevalence strictly above no_collapse_above_prevalence or absolute count strictly above no_collapse_above_absolute are retained, however. For factor variables, these are collapsed to the next larger level, for ordered variables, rare variables are collapsed to the neighbouring class, whichever has fewer samples. In case both no_collapse_above_prevalence and no_collapse_above_absolute are given, the less strict threshold of the two will be used, i.e. if no_collapse_above_prevalence is 1 and no_collapse_above_absolute is 10 for a task with 100 samples, levels that are seen more than 10 times will not be collapsed.

Levels not seen during training are not touched during prediction; Therefore it is useful to combine this with the PipeOpFixFactors.

Format

R6Class object inheriting from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

Construction

PipeOpCollapseFactors$new(id = "collapsefactors", param_vals = list())

• id :: character(1)
  Identifier of resulting object, default "collapsefactors".

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

Input and Output Channels

Input and output channels are inherited from PipeOpTaskPreproc.

The output is the input Task with rare affected factor and ordered feature levels collapsed.

State

The $state is a named list with the $state elements inherited from PipeOpTaskPreproc, as well as:

• collapse_map :: named list of named list of character
  List of factor level maps. For each factor, collapse_map contains a named list that indicates what levels of the input task get mapped to what levels of the output task. If collapse_map has an entry feat_1 with an entry a = c("x", "y"), it means that levels "x" and "y" get collapsed to level "a" in feature "feat_1".

Parameters

The parameters are the parameters inherited from PipeOpTaskPreproc, as well as:

• no_collapse_above_prevalence :: numeric(1)
  Fraction of samples below which factor levels get collapsed. Default is 1, which causes all levels to be collapsed until target_level_count remain.

• no_collapse_above_absolute :: integer(1)
  Number of samples below which factor levels get collapsed. Default is Inf, which causes all levels to be collapsed until target_level_count remain.

• target_level_count :: integer(1)
  Number of levels to retain. Default is 2.

Internals

Makes use of the fact that levels(fact_var) = list(target1 = c("source1", "source2"), target2 = "source2") causes renaming of level "source1" and "source2" both to "target1", and also "source2" to "target2".

Fields

Only fields inherited from PipeOp.

Methods

Only methods inherited from PipeOpTaskPreprocSimple/PipeOpTaskPreproc/PipeOp.

See also

https://mlr-org.com/pipeops.html

Other PipeOps: PipeOp, PipeOpEncodePL, PipeOpEnsemble, PipeOpImpute, PipeOpTargetTrafo, PipeOpTaskPreproc, PipeOpTaskPreprocSimple, mlr_pipeops, mlr_pipeops_adas, mlr_pipeops_blsmote, mlr_pipeops_boxcox, mlr_pipeops_branch, mlr_pipeops_chunk, mlr_pipeops_classbalancing, mlr_pipeops_classifavg, mlr_pipeops_classweights, mlr_pipeops_colapply, mlr_pipeops_colroles, mlr_pipeops_copy, mlr_pipeops_datefeatures, mlr_pipeops_decode, mlr_pipeops_encode, mlr_pipeops_encodeimpact, mlr_pipeops_encodelmer, mlr_pipeops_encodeplquantiles, mlr_pipeops_encodepltree, mlr_pipeops_featureunion, mlr_pipeops_filter, mlr_pipeops_fixfactors, mlr_pipeops_histbin, mlr_pipeops_ica, mlr_pipeops_imputeconstant, mlr_pipeops_imputehist, mlr_pipeops_imputelearner, mlr_pipeops_imputemean, mlr_pipeops_imputemedian, mlr_pipeops_imputemode, mlr_pipeops_imputeoor, mlr_pipeops_imputesample, mlr_pipeops_kernelpca, mlr_pipeops_learner, mlr_pipeops_learner_pi_cvplus, mlr_pipeops_learner_quantiles, mlr_pipeops_missind, mlr_pipeops_modelmatrix, mlr_pipeops_multiplicityexply, mlr_pipeops_multiplicityimply, mlr_pipeops_mutate, mlr_pipeops_nearmiss, mlr_pipeops_nmf, mlr_pipeops_nop, mlr_pipeops_ovrsplit, mlr_pipeops_ovrunite, mlr_pipeops_pca, mlr_pipeops_proxy, mlr_pipeops_quantilebin, mlr_pipeops_randomprojection, mlr_pipeops_randomresponse, mlr_pipeops_regravg, mlr_pipeops_removeconstants, mlr_pipeops_renamecolumns, mlr_pipeops_replicate, mlr_pipeops_rowapply, mlr_pipeops_scale, mlr_pipeops_scalemaxabs, mlr_pipeops_scalerange, mlr_pipeops_select, mlr_pipeops_smote, mlr_pipeops_smotenc, mlr_pipeops_spatialsign, mlr_pipeops_subsample, mlr_pipeops_targetinvert, mlr_pipeops_targetmutate, mlr_pipeops_targettrafoscalerange, mlr_pipeops_textvectorizer, mlr_pipeops_threshold, mlr_pipeops_tomek, mlr_pipeops_tunethreshold, mlr_pipeops_unbranch, mlr_pipeops_updatetarget, mlr_pipeops_vtreat, mlr_pipeops_yeojohnson

Examples

library("mlr3")
op = PipeOpCollapseFactors$new()

# Create example training task
df = data.frame(
  target = runif(100),
  fct = factor(rep(LETTERS[1:6], times = c(25, 30, 5, 15, 5, 20))),
  ord = factor(rep(1:6, times = c(20, 25, 30, 5, 5, 15)), ordered = TRUE)
)
task = TaskRegr$new(df, target = "target", id = "example_train")

# Training
train_task_collapsed = op$train(list(task))[[1]]
train_task_collapsed$levels(c("fct", "ord"))
#> $fct
#> [1] "A" "B"
#> 
#> $ord
#> [1] "2" "3"
#> 

# Create example prediction task
df_pred = data.frame(
  target = runif(7),
  fct = factor(LETTERS[1:7]),
  ord = factor(1:7, ordered = TRUE)
)
pred_task = TaskRegr$new(df_pred, target = "target", id = "example_pred")

# Prediction
pred_task_collapsed = op$predict(list(pred_task))[[1]]
pred_task_collapsed$levels(c("fct", "ord"))
#> $fct
#> [1] "A" "B" "G"
#> 
#> $ord
#> [1] "2" "3" "7"
#>